Knowledge Commons of Institute of Automation,CAS
Language-Adversarial Transfer Learning for Low-Resource Speech Recognition | |
Yi, Jiangyan; Tao, Jianhua; Wen, Zhengqi; Bai, Ye | |
发表期刊 | IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING |
ISSN | 2329-9290 |
2019-03-01 | |
卷号 | 27期号:3页码:621-630 |
摘要 | The acoustic model trained using the knowledge from the shared hidden layer (SHL) model outperforms the model trained only by using the target language, especially under low resource conditions. However, the shared features may contain some unnecessary language dependent information. It will degrade the performance of the target model. Therefore, this paper proposes language-adversarial transfer learning to alleviate this problem. Adversarial learning is used to ensure that the shared layers of the SHL-model can learn more language invariant features. Experiments are conducted on IARPA Babel datasets. The results show that the target model trained using the knowledge transferred from the adversarial SHL-model achieves up to 10.1% relative word error rate reduction when compared with the target model trained using the knowledge transferred from the SHL-model. |
关键词 | Adversarial training transfer learning cross-lingual low-resource speech recognition |
DOI | 10.1109/TASLP.2018.2889606 |
关键词[WOS] | DEEP NEURAL-NETWORKS ; ACOUSTIC MODELS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Inria-CAS Joint Research Project[173211KYSB20170061] ; National Natural Science Foundation of China (NSFC)[61771472] ; National Natural Science Foundation of China (NSFC)[61603390] ; National Natural Science Foundation of China (NSFC)[61773379] ; National Natural Science Foundation of China (NSFC)[61425017] ; National Key Research and Development Plan of China[2017YFC0820602] ; National Key Research and Development Plan of China[2017YFC0820602] ; National Natural Science Foundation of China (NSFC)[61425017] ; National Natural Science Foundation of China (NSFC)[61773379] ; National Natural Science Foundation of China (NSFC)[61603390] ; National Natural Science Foundation of China (NSFC)[61771472] ; Inria-CAS Joint Research Project[173211KYSB20170061] |
WOS研究方向 | Acoustics ; Engineering |
WOS类目 | Acoustics ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000457913900001 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 语音识别与合成 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/25290 |
专题 | 多模态人工智能系统全国重点实验室_智能交互 |
通讯作者 | Tao, Jianhua |
作者单位 | Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Yi, Jiangyan,Tao, Jianhua,Wen, Zhengqi,et al. Language-Adversarial Transfer Learning for Low-Resource Speech Recognition[J]. IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,2019,27(3):621-630. |
APA | Yi, Jiangyan,Tao, Jianhua,Wen, Zhengqi,&Bai, Ye.(2019).Language-Adversarial Transfer Learning for Low-Resource Speech Recognition.IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,27(3),621-630. |
MLA | Yi, Jiangyan,et al."Language-Adversarial Transfer Learning for Low-Resource Speech Recognition".IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 27.3(2019):621-630. |
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